Individual Prediction of Cognitive Decline in Mild Cognitive Impairment Using Support Vector Machine-Based Analysis of Diffusion Tensor Imaging Data

Article type: Research Article

Authors: Haller, Sven^{a; *} | Nguyen, Duy^a | Rodriguez, Cristelle^b | Emch, Joan^b | Gold, Gabriel^c | Bartsch, Andreas^d | Lovblad, Karl O.^a | Giannakopoulos, Panteleimon^{b; e}

Affiliations: [a] Service neuro-diagnostique et neuro-interventionnel DISIM, University Hospitals of Geneva, Geneva, Switzerland | [b] Division of Geriatric Psychiatry, Department of Psychiatry, University Hospitals of Geneva and Faculty of Medicine, University of Geneva, Geneva, Switzerland | [c] Department of Rehabilitation and Geriatrics, University Hospitals of Geneva, and Faculty of Medicine, University of Geneva, Geneva, Switzerland | [d] Department of Neuroradiology, University Hospital of Heidelberg, Heidelberg, Germany | [e] Division of Old Age Psychiatry (PG), University of Lausanne School of Medicine, Lausanne, Switzerland

Correspondence: [*] Corresponding to: Dr. M.Sc. Sven Haller, Service neuro-diagnostique et neuro-interventionnel DISIM, Hôpitaux Universitaires de Genève, Rue Gabrielle Perret-Gentil 4, 1211 Genève 14, Switzerland. Tel.: +41 (0) 22 37 23311; Fax: +41 (0) 22 37 27072; E-mail: sven.haller@hcuge.ch.

Note: [] Handling Associate Editor: Kurt Jellinger

Abstract: Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Keywords: Diffusion tensor imaging, fractional anisotrophy, mild cognitive impairment, support vector machines, Tract-Based Spatial Statistics

DOI: 10.3233/JAD-2010-100840

Journal: Journal of Alzheimer's Disease, vol. 22, no. 1, pp. 315-327, 2010